This invention is a process for the selection and evaluation of investment portfolio asset allocation strategies based on the quantitative analysis of past investment performance. This process relates to the collection and analysis of comparative statistics for whole populations of allocation alternatives over a series of time-periods.
The present invention relates to the replacement of existing quantitative processes of selection and evaluation that rely on algorithmic methods to generate a solution set of allocation strategies based either on the analysis of small, highly volatile groups within a population of allocation-alternatives or on projections of future economic conditions and their effect on asset class performance.
In contrast to these algorithmic approaches, the process of the present invention relates to a data-base approach that works from the analysis of series of statistics of population dynamics over time. This procedure is a much more labor and computer-intensive procedure than those based on algorithmic solutions. This is a part of the reason why it has not been developed before. Its legitimacy, however, can be validated by virtue of its ability to call into question the utility of several popular industry practices regarding strategy selection that have been developed from these algorithmic processes.
The process of the present invention relates more specifically for use by a “conservative investor” otherwise known as an investor who has become skeptical of the value of investment advice based on the ability to predict the future. These conservative investors are, by definition, the users of advice regarding asset allocation strategies. Its business use is predicated on the belief that such investors value stability and place highest priority on allocation strategies whose investment performance remains most stable over changes in market conditions.
These investors are ill-served by solution sets derived from known prior art algorithmic processes, because these processes fail to account for the effect of changes in market conditions. Changes in market conditions occur with such regularity and frequency as to be commonly known as “market cycles”. Therefore, there is a need for a process that can compare whole populations of alternatives across multiple market-cycle phases. It is characteristic of investors to hold their investment assets as portfolios and to allocate the assets of those portfolios among investment alternatives of diverse risk in order to mitigate the effects of these market cycles on portfolio investment performance. Therefore, a process is desired that is uniquely relevant to the uses of an investor that holds their investable assets as a portfolio and employs asset allocation as a part of their investment strategy.
It is well known that investors, unless they possess perfect foresight regarding the future path of investment markets and of the performance of individual investments, will seek to hold more than one investment at a point in time, in order to hedge against future investment-performance volatility arising from a less-than-perfect selection. Holding more than one investment creates an “investment portfolio”. Such a hedging process is commonly referred to as “asset diversification”, and the formulation of a plan for asset diversification is commonly referred to as “selecting an asset allocation strategy”. Selecting a strategy, to the extent that it is based on a quantified analysis of alternatives, will entail either the comparison of alternatives in terms of the characteristics of their past investment performance or the projection of future investment performance based on a forecast of future market conditions and their predicted effect on the performance of the investments within a portfolio.
Selecting an asset allocation strategy is one of three basic functions necessary for selecting and maintaining an investment portfolio. An asset allocation strategy is set out in terms of percentages of portfolio assets to be held in investments from each market sector. The $40 trillion global market for publicly traded securities can be segmented into a small handful of “market sectors” which are groups of securities who, over time, have demonstrated a similar pattern of investment risk that has been uniquely different from securities in other sectors. These groups of securities are commonly known as “asset classes”. Once this allocation strategy has been formulated, the other two management functions are to 1) select for investments within each sector that together will provide a diversified risk; 2) select for investments whose future investment performance will be strong relative to their sector peers.
In 1952, Dr. Harry Markowitz published a thesis entitled, Modern Portfolio Theory (MPT), in which he proved, by use of an algorithm, that the value of such asset diversification efforts would be maximized by combining investment assets from market sectors whose patterns and levels of investment risk had been uniquely different over time. The results of this algorithm are to define a sub-population of allocation strategy alternatives, known as an “efficient frontier”, and a line, known as the “efficiency-line”, that identify those asset allocation strategies that generated the maximum investment return across a range of investment risk available within a population of allocation-strategy alternatives in a prior time-period.
Dr. Markowitz also created two other definitions that have become central to the practice of investment management and are germane to this patent application. He defined the concept of “investment performance” as the product of two variables which is investment returns and investment risk where that relationship is plotted as a two-dimension graph with the y-axis marking investment return as the average of a series of contiguous periodic returns and the x-axis marking investment risk as the variance of those periodic returns around their average. This is known as a “mean-variance” graph. He also defined the objective of the investor as maximizing the investment returns available for a level of investment risk. His “efficient-frontier” is the solution-set of allocation alternatives that satisfy this objective.
FIGS. 1 and 2 illustrate Dr. Markowitz” known prior art concepts of investment performance, mean-variance graph and an efficient frontier for a population of allocation alternatives. To the extent that processes involving the quantitative analysis of past-period investment performance statistics are used for selecting or evaluating asset allocation strategies, the investment management industry has exclusively adopted processes based on Dr. Markowitz” algorithm and the identification of the population of allocation alternatives residing on this frontier at a given point in time.
The usefulness of the allocation alternatives whose investment performance is identified as resident upon an efficiency-line in a past period is conditioned by assumptions regarding the stability of that residency in future periods. As will be described and illustrated in detail below, these assumptions made by prior art methods are flawed.
For those investors who have likewise become skeptical of the utility of the efficiency-line processes, there currently exists a more proactive algorithmic approach designed to forecast future market trends and anticipate their effects on asset-class performance. This approach relies on two algorithmic processes. The first “decomposes” the past investment performance of available investments into a weighted sum of two or more “market factors”. These market factors are commonly market indices, such as the S&P500 Market Index. The attribution of market-index performance statistics to the performance of an individual investment is an algorithmic process called “factor analysis”. A “pricing kernel”, a second algorithm, projects the future path of markets and their effect on the investment performance of these market indices.
There exist investors that are also skeptical of the utility of this process, based on the general track record of economists and other analysts in forecasting future economic and market conditions, and the recent troubles of Long Term Capital Corp. and other commercial ventures in applying this technology to the selection of investments and allocation strategies. For these investors, another option is needed for selecting a strategy to allocate the assets of their investment portfolios that overcomes the shortcomings found in prior art methods.
In the investment field, there are problems involved with the acceptance by investors of the “pricing-kernel” algorithmic process. Beyond its poor record in commercial application, there exists the illogical nature of applying it to a process of asset diversification. In the first instance, if the provider of such a process could indeed predict the future path of markets and investment performance, there would exist no need to diversify because the investment recommendation could be limited to a single optimal investment. Secondly, if a provider could in fact predict the future, he would, for obvious reasons, forego sharing this information with others.
These processes, in fact, cannot generate a specific recommendation, but are cast as a “stochastic solution” which is a probability array of future choices. The value of these probability arrays depends on the assumption that the probable path of future market cycles follows a normal, or at least, a symmetrical distribution. It has been shown in the past that the actual path of market cycles often deviates from this assumption, and as the troubles of Long Term Capital Corp have aptly demonstrated results in loss of funds.
There have been many attempts in the prior art to solve the aforementioned problems. There exist several patents for processes to select allocation strategies based on these predictive algorithms.
This process for analyzing comparative investment performance statistics among a population of allocation alternatives is not unlike the analyses that have historically been generated in regard to comparing investments within an asset class, which is the population of investments within a market sector. Averages for investment return and risk are commonly computed from population data for a specified analysis-period, and the performance of individuals within each population is compared to these averages. A series of analysis-period analyses are then compared by calculating an average and variance for statistics that they hold in common and the time-period trends so identified are tested for statistical relevance.
However, the prior art have not applied these processes to populations of allocation alternatives. The prior art is devoid of such methodologies. Prior methods for describing allocation alternative populations that have taken as their basis the MPT efficiency-line algorithm and processes derived from this algorithm cannot analyze performance differences among populations of allocation alternatives because they cannot see those populations of alternatives or track them over time.
Due to lack of computing power in the past, the use of the MPT algorithm was desirable because it used little computing-resources, as long as one keeps small the number of market-sectors under consideration. The algorithm involves terms for the pair-wise covariant relationship between market-sectors, namely five market sectors involves calculating ten pair-wise covariant terms; ten market sectors involve calculating forty-five covariant terms.
Defining an allocation-alternative population by means of a data base of computed investment return and risk for each allocation alternative is much more computer and labor-intensive. The population of allocation alternatives arising from five market sectors combined in 5-percent increments is 10,626; for ten market sectors, the population of alternatives is 7.75 million.
There is a desire for a method to work on commercially available spreadsheet and database software, such as Microsoft Excel and Access and a PC of that is configured for the maximum available memory, storage and computing speed. At a five marketsector population, the capacity of the software and current PC computer hardware is taxed, requiring multiple sub-steps to accumulating the performance statistics for a 10,626-item population. Without careful adherence to these sub-steps, the scope of the calculations is beyond the capacity of the machinery. However, present day consumer computers are can perform the desired calculations. Thus, modern computing technology has enabled large number processing possible at low cost.
Processes related to investment management have been granted patents only over the last few years. Until recently, the existence of innovations and existing practices in the field has been communicated primarily through publication in academic journals and textbooks on financial economics. A review of prior art must include these sources as well as the patent record.
Modern Portfolio Theory and Investment Analysis, by E. Elton and M. Gruber, 1995, teaches two methods for asset allocation selection. Both begin with the calculation of an “efficient frontier” in the manner as outlined previously in this paper. For a portfolio comprised of only “risky-assets”, it recommends selecting allocation alternatives that reside on the efficiency-line made from market sectors containing those risky assets and at a level of returns variance commensurate with the investor”s risk tolerance. For a portfolio comprise of both risky and riskless assets, it recommends finding the most efficient of the allocation alternatives made from the risky assets, known as the “market portfolio”, and then amending that allocation strategy with the addition of riskless assets to achieve a level of returns variance commensurate with the investor”s risk tolerance. The expected performance from such an amended strategy can be located on a mean-variance graph as a straight line drawn from the point of average return for the riskless asset through the point of return for the market portfolio, the line being known as a “market line”, FIG. 2. The Elton and Gruber exposition of the population of allocation alternative available to an investor never references the remainder of the allocation population that resides off the efficiency line.
The methodology of selecting for an allocation alternative from an efficiency line or market portfolio population is one that works much better in theory than in practice. As my process can demonstrate, the future investment performance for an allocation-alternative selected from an efficiency line for a series of selection periods over the last forty years has been poor relative to its allocation population peers. Picking a single point from among an efficiency-line population compounds the problem, generating a selection of even a poorer record of relative future performance and further compromising a process whose purpose is to ensure that the future strength and stability of an investment portfolio”s performance will meet investor expectations.
The prior art also includes articles within the research journals over the last fifty years that explore allocation strategy selection. Many of these articles advocate for adding one or more explanatory variables to the prior-period measurements of investment return and risk that go into creating the efficient-frontier population, in effect changing the two-dimensional efficiency line into a three dimensional cone. The jury is still out over whether adding additional factors to an efficiency-line creates a solution to the future performance problems arising from such selections. However, the important point is that in all of its machinations, the prior art never once references the allocation alternatives that may lie inside this line or cone.
A number of prior art patents exist for processes related the function of asset allocation strategy selection and evaluation. Some related to predictive algorithms, a pricing kernel or otherwise “hidden algorithm” being an integral part of their process. Other prior art methods employ processes other than strategy selection and evaluation, and default to “external factors” for the identification of an allocation strategy. Additionally, other prior art methods use a process built off the procedure of finding an efficiency-line population of allocation alternatives.
As example of predictive algorithms, U.S. Pat. No. 6,021,397 issued to Jones, et al. and U.S. Pat. No. 6,125,355 issued to Bekaert, et al. decompose the past performance of investment alternatives into a set of factors made from market indices and then predict the future path of investment markets and their effect on the performance of these factors using a pricing kernel of unspecified dimensions. As example of selection and evaluation processes built from an efficiency-line populations, U.S. Pat. No. 6,078,904, issued to Rebane, identifies a single point of maximum differential returns from among a set of allocation strategies that reside on an efficiency line. Also, U.S. Pat. No. 6,003,018, issued to Michuad et al., employs a process that evaluates the continuing suitability of allocation alternatives selected by an efficiency-line by a “resampling procedure” which runs a number of economic simulations to create a probability distribution around each point on the line.
In view of the prior art, the primary need is to correct for erroneous and misleading information generated by existing algorithmic processes using an efficiency line. These processes are the ones most generally in use within the market, and their shortcomings the most damaging to the interests of investors.
Prior art algorithmic processes, which are based on the analysis of an efficiency-line population, are deficient at two levels. First, the Markowitz algorithm used in such analyses identifies only a small portion of the allocation alternatives available. If an investor decides for an allocation alternative that does not appear on this “efficiency line” for a prior-period, the question arises as to how to identify its relative value.
For example, of the total population of allocation alternatives that can be constructed from a population of market sectors, less than 1% of those alternatives will reside on the efficiency line for any given time period. For the analysis of the investment performance for the other 99% of a population of allocation alternatives, the data generated by an algorithm defining an efficiency line will fail to assist the investor.
The second, and more critical issue is that the alternatives identified through their residency upon an efficiency line in a prior-period are those least likely to be relevant to the purposes of an investor. The investor”s objective of maximizing returns for a level of risk implies that an allocation-alternative”s position of an efficiency line in a prior period must have some relation to the chance of that allocation-alternative residing upon an efficiency line or at least remaining a strong alternative in future periods.
This issue is compounded by the fact that an asset allocation strategy is, in practice, a process that is only relevant in solving for the issue of “long-term” investment performance and risk. Economists have found that market conditions are cyclical and change over time, and that the relative strength of various market sectors, and of the various combinations of market sectors, shifts with these changes. The population of allocation alternatives residing on an efficiency line in a past analysis-period that documents one phase of a market cycle is comprised of those allocation alternatives most responsive to the specific market conditions of that cycle phase. As market conditions changes, these alternatives are the ones within the population that are least likely to thrive in a transition of markets to another phase of a cycle.
It is therefore a principal object of this invention to provide a method of selecting and evaluating investments to generate a solution set of allocation strategies.
It is also an object of the invention to employ a database approach to selecting investments that is based on the analysis of a series of statistics of population dynamics over time.
Another principal object is to provide a method with the foregoing advantages that is strongly predictive of future differences in investment performance characteristics among populations of allocation alternatives, and is reliable regardless of market conditions, and produces a magnitude of superior future performance that justifies the cost of practicing the selection process.